Difference between revisions of "Fitzwilliam Museum"

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Contact: Joanne Vine <jrv31@cam.ac.uk>
 
Contact: Joanne Vine <jrv31@cam.ac.uk>
 
Daniel Pett <dejp3@cam.ac.uk>
 
Daniel Pett <dejp3@cam.ac.uk>
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I’m interested in quite a few things based around collections and data:
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1) Machine learning and computer vision - the work my former colleague, Harrison Pim is doing at the Wellcome is along the lines of where I want to go eg https://twitter.com/hmpim/status/1052961056727990272 Very python driven research or R.
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2) Predictive modelling of exhibition attendance
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3) Data science stuff around social media and reviews data from sources like Tripadvisor
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4) 3D modelling approaches
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5) Visualisation of any sort
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6) expansion of my crowdsourcing project https://crowdsourced.micropasts.org
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1) Harrison Pim's work is probably more the kind of thing that we might get a Master's student to do. Undergraduates could probably have a good hack at it, but computer resources are higher than they usually have, it can often take longer to negotiate access to labelled data than the span of the project, and it's hard to divide this kind of work among a team - as you say, usually just one person at a Python console (followed by waiting three days for the network to be trained).
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2) Predictive modelling of exhibition attendance would have results largely driven by the quality of your dataset. What kind of real-time data do you have access to, and is there a substantial amount of historical data for building the predictive model?
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3)  It's an interesting idea to work with Tripadvisor. Do Cambridge museums pick up a lot of reviews there? Do you happen to know anything about their APIs and terms of service for data mining research?
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4) I presume you mean 3D models of collection objects. Do you have a laser scanner that could be used for the digitisation stage? We don't have one in the Computer Lab, but have collaborated with other museums in the past who used their own scanners, for example in NHM.
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5) Past experience is that CS undergraduates are not that good at inventing novel visualisations. My research group does a lot of visualisation research, but for undergrads, we would probably have to specify exactly what we want their system to look like.
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6) That's a nice project. Which of the datasets have attracted most labelling input? Is this in a form that validation and reliability analysis might be useful?
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After meeting with Andrea Kells, "... feels that there might be scope for the Museum to offer one or more projects – for example around digital curation, monitoring visitor interaction with exhibits/visitor flow, using social media and other sources to collate and analyse visitor feedback etc."
 
After meeting with Andrea Kells, "... feels that there might be scope for the Museum to offer one or more projects – for example around digital curation, monitoring visitor interaction with exhibits/visitor flow, using social media and other sources to collate and analyse visitor feedback etc."

Revision as of 14:33, 30 October 2018

Contact: Joanne Vine <jrv31@cam.ac.uk> Daniel Pett <dejp3@cam.ac.uk>


I’m interested in quite a few things based around collections and data:

1) Machine learning and computer vision - the work my former colleague, Harrison Pim is doing at the Wellcome is along the lines of where I want to go eg https://twitter.com/hmpim/status/1052961056727990272 Very python driven research or R. 2) Predictive modelling of exhibition attendance 3) Data science stuff around social media and reviews data from sources like Tripadvisor 4) 3D modelling approaches 5) Visualisation of any sort 6) expansion of my crowdsourcing project https://crowdsourced.micropasts.org

1) Harrison Pim's work is probably more the kind of thing that we might get a Master's student to do. Undergraduates could probably have a good hack at it, but computer resources are higher than they usually have, it can often take longer to negotiate access to labelled data than the span of the project, and it's hard to divide this kind of work among a team - as you say, usually just one person at a Python console (followed by waiting three days for the network to be trained).

2) Predictive modelling of exhibition attendance would have results largely driven by the quality of your dataset. What kind of real-time data do you have access to, and is there a substantial amount of historical data for building the predictive model?

3) It's an interesting idea to work with Tripadvisor. Do Cambridge museums pick up a lot of reviews there? Do you happen to know anything about their APIs and terms of service for data mining research?

4) I presume you mean 3D models of collection objects. Do you have a laser scanner that could be used for the digitisation stage? We don't have one in the Computer Lab, but have collaborated with other museums in the past who used their own scanners, for example in NHM.

5) Past experience is that CS undergraduates are not that good at inventing novel visualisations. My research group does a lot of visualisation research, but for undergrads, we would probably have to specify exactly what we want their system to look like.

6) That's a nice project. Which of the datasets have attracted most labelling input? Is this in a form that validation and reliability analysis might be useful?



After meeting with Andrea Kells, "... feels that there might be scope for the Museum to offer one or more projects – for example around digital curation, monitoring visitor interaction with exhibits/visitor flow, using social media and other sources to collate and analyse visitor feedback etc."

Previous projects (see also Hamilton Kerr Institute, Cambridge Museums):

  • Next-Generation Museum Guide - in 2008 and 2006
  • Smart Poster Picker - in 2008